@InProceedings{reed-EtAl:2017:Short,
  author    = {Reed, Lena  and  Wu, Jiaqi  and  Oraby, Shereen  and  Anand, Pranav  and  Walker, Marilyn},
  title     = {Learning Lexico-Functional Patterns for First-Person Affect},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {141--147},
  abstract  = {Informal first-person narratives are a unique resource for computational mod-
	els of everyday events and people’s affective reactions to them. People
	blogging about their day tend not to explicitly say I am happy. Instead they
	describe situations from which other humans can readily infer their affective
	reactions. However current sentiment dictionaries are missing much of the
	information needed to make similar inferences. We build on recent work that
	models affect in terms of lexical predicate functions and affect on the
	predicate’s arguments. We present a method to learn proxies for these
	functions from first- person narratives. We construct a novel fine-grained test
	set, and show that the pat- terns we learn improve our ability to pre- dict
	first-person affective reactions to everyday events, from a Stanford sentiment
	baseline of .67F to .75F.},
  url       = {http://aclweb.org/anthology/P17-2022}
}